Secure communication in CloudIoT through design of a lightweight authentication and session key agreement scheme

Internet of Things (IoT) is a newly emerged paradigm where multiple embedded devices, known as things, are connected via the Internet to collect, share, and analyze data from the environment. In order to overcome the limited storage and processing capacity constraint of IoT devices, it is now possible to integrate them with cloud servers as large resource pools. Such integration, though bringing applicability of IoT in many domains, raises concerns regarding the authentication of these devices while establishing secure communications to cloud servers. Recently, Kumari et al proposed an authentication scheme based on elliptic curve cryptography (ECC) for IoT and cloud servers and claimed that it satisfies all security requirements and is secure against various attacks. In this paper, we first prove that the scheme of Kumari et al is susceptible to various attacks, including the replay attack and stolen-verifier attack. We then propose a lightweight authentication protocol for secure communication of IoT embedded devices and cloud servers. The proposed scheme is proved to provide essential security requirements such as mutual authentication, device anonymity, and perfect forward secrecy and is robust against security attacks. We also formally verify the security of the proposed protocol using BAN logic and also the Scyther tool. We also evaluate the computation and communication costs of the proposed scheme and demonstrate that the proposed scheme incurs minimum computation and communication overhead, compared to related schemes, making it suitable for IoT environments with low processing and storage capacity.     

Modeling the heat of vaporization of petroleum fractions and pure hydrocarbons

The present contribution was aimed to estimate the vaporization enthalpy of petroleum fractions and pure hydrocarbons by using the combination of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) called GA-ANFIS. This tool can approximate the vaporization enthalpy as a function of the specific gravity, molecular weight, and boiling point temperature with high accuracy based on 122 data gathered from the previously published literature. Furthermore, results from the proposed model have been compared with different correlations and its acceptable predictive ability against other correlations was proved in order to the estimation of the vaporization enthalpy. The percentage of absolute relative deviation and R-squared (R²) was 1.64% and 0.9967%, respectively. This tool is simple to use and can be of considerable help for petroleum engineers to have an accurate estimation of vaporization enthalpy of hydrocarbon fractions of pure hydrocarbons.     

High ion conducting polymer nanocomposite electrolytes using hybrid nanofillers

There is a growing shift from liquid electrolytes toward solid polymer electrolytes, in energy storage devices, due to the many advantages of the latter such as enhanced safety, flexibility, and manufacturability. The main issue with polymer electrolytes is their lower ionic conductivity compared to that of liquid electrolytes. Nanoscale fillers such as silica and alumina nanoparticles are known to enhance the ionic conductivity of polymer electrolytes. Although carbon nanotubes have been used as fillers for polymers in various applications, they have not yet been used in polymer electrolytes as they are conductive and can pose the risk of electrical shorting. In this study, we show that nanotubes can be packaged within insulating clay layers to form effective 3D nanofillers. We show that such hybrid nanofillers increase the lithium ion conductivity of PEO electrolyte by almost 2 orders of magnitude. Furthermore, significant improvement in mechanical properties were observed where only 5 wt % addition of the filler led to 160% increase in the tensile strength of the polymer. This new approach of embedding conducting-insulating hybrid nanofillers could lead to the development of a new generation of polymer nanocomposite electrolytes with high ion conductivity and improved mechanical properties.     

Quantum dots: synthesis, bioapplications, and toxicity

Ferrite nanoparticles of basic composition Ni_0.7-x Zn _x Cu_0.3Fe₂O₄ (0.0 ?? x ?? 0.2, x = 0.05) were synthesized through auto-combustion method and were characterized for structural properties using X-ray diffraction [XRD], scanning electron microscopy, transmission electron microscopy, and Fourier transform infrared spectroscopy [FT-IR]. XRD analysis of the powder samples sintered at 600°C for 4 h showed the cubic spinel structure for ferrites with a narrow size distribution from 28 to 32 nm. FT-IR showed two absorption bands (v ₁ and v ₂) that are attributed to the stretching vibration of tetrahedral and octahedral sites. The effect of Zn doping on the electrical properties was studied using dielectric and impedance spectroscopy at room temperature. The dielectric parameters (??', ????, tan??, and ?? _ac) show their maximum value for 10% Zn doping. The dielectric constant and loss tangent decrease with increasing frequency of the applied field. The results are explained in the light of dielectric polarization which is similar to the conduction phenomenon. The complex impedance shows that the conduction process in grown nanoparticles takes place predominantly through grain boundary volume. PACS: 75.50.Gg; 78.20; 77.22.Gm.     

Quantifying and cancelation memory effects in high power amplifier for OFDM systems

A modeling approach to power amplifier design for implementation in OFDM radio units is presented. The power amplifier model assesses the impact of linear memory effects within the system using a Wiener representation, and employs a linear novel parametric estimation technique using Hilbert space. In addition, in order to model the nonlinear memory effects the previous topology is generalized by inserting the truncated Volterra filter before the static nonlinearity. Predistortion based on the Hammerstein model is introduced to deal with the nonlinear response. The new general algorithm is proposed to evaluate the Hammerstein model parameters for an OFDM system. A representative test bed was designed and implemented. The assessment of the new methods for PA and PD modeling are confirmed by experimental measurements. The measurement results reveal the preference of the new techniques over the existing approaches.     

Attributions as inferences and explanations: Conjunction effects.

When asked to estimate the probability of an interpretation for an event, observers may either assess whether the event can give rise to the interpretation (an inference set) or assess whether the interpretation can give rise to the event (an explanation set). These two strategies may moderate the conjunction effects (Leddo, Abelson, & Gross, 1984)—attributors' tendency to assign lower probabilities to single-reason interpretations than to their conjunctions. Our two studies showed that explanation-set instructions (e.g., "assess the probability that the interpretation could be the reason for the event") produced stronger conjunction effects than inference-set instructions (e.g., "assess the probability that the interpretation could be inferred from the event"). This Set (inference or explanation)?×?Reason (multiple or single) interaction was not affected by whether the events involved voluntary or involuntary behavior or by whether they described events that happened or failed to happen. In a third study, we found that subjects in an inference set were more likely to report that they estimated probability of the interpretation (as opposed to probability of the behavior) than were subjects in an explanation set. The extent to which the explanation set may account for conjunction effects obtained in other studies was discussed. Possible implications and determinants of the inference-explanation distinction were also considered. (19 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Artificial Neural Network estimation of wheel rolling resistance in clay loam soil

Despite of complex and nonlinear relationships imparting soil–wheel interactions, however, logical, non-randomized, and manifold relations tackle to express and model the interactions which are valid for variety of conditions and are likely to be established whereas mathematical equations are restricted to present. A 3-10-1 feed-forward Artificial Neural Network (ANN) with back propagation (BP) learning algorithm was utilized to estimate the rolling resistance of wheel as affected by velocity, tire inflation pressure, and normal load acting on wheel inside the soil bin facility creating controlled condition for test run. The model represented mean squared error MSE of 0.0257 and predicted relative error values with less than 10% and high coefficient of determination (R²) equal to 0.9322 utilizing experimental output data obtained from single-wheel tester of soil bin facility. These rewarding outcomes signify the fitting exploit of ANN for prediction of rolling resistance as a practical model with high accuracy in clay loam soil. Derived data revealed rolling resistance is less affected by applicable velocities of tractors in farmlands nevertheless is much influenced by inflation pressure and vertical load. An approximate constant relationship existed between velocity and rolling resistance implying that rolling resistance is not function of velocity chiefly in lower ones. Increase of inflation pressure results in decrease of rolling resistance while increase of vertical load brings about increase of rolling resistance which was measured to be function of vertical load by polynomial with order of two model validated by conventional models such as Wismer and Luth model.     

Development of a screening test based on isothermal calorimetry for determination of self‐heating potential of biomass pellets

For the risk for spontaneous combustion in storage of biomass pellets to be assessed, it is important to know how prone the fuel is to self‐heating. There are traditional methods that are used to determine self‐heating characteristics of fuels, eg, basket heating tests. The results from basket heating tests indirectly give the reactivity from a series of tests at high temperatures. This paper presents a sensitive screening test procedure for biomass pellets using isothermal calorimetry for direct measurement of the heat production rate at typical bulk storage temperatures. This method can be used to directly compare the reactivity of different batches of biomass pellets. The results could be used, eg, by storage security managers to gain better knowledge of their fuels propensity for self‐heating and thereby for safer storage. A large number of tests have been performed to develop the test procedure presented. Different parameters, such as temperature, type of the test sample (powder/crushed or pellets), mass of test sample, and preheating time, have been varied. Furthermore, gas concentrations in the sample ampoule have been measured before and after some tests to study the oxygen consumption and the formation of CO and CO₂. Three different types of pellets with different characteristics were tested to assess the variation in behaviour. Based on these tests, a screening test procedure is presented with a test temperature of 60°C, a sample size of 4 g, a 15‐minute preheating period at the test temperature, and 24‐hour test duration.     

Synthesis, contact printing, and device characterization of Ni-catalyzed, crystalline InAs nanowires

InAs nanowires have been actively explored as the channel material for high performance transistors owing to their high electron mobility and ease of ohmic metal contact formation. The catalytic growth of nonepitaxial InAs nanowires, however, has often relied on the use of Au colloids which is non-CMOS compatible. Here, we demonstrate the successful synthesis of crystalline InAs nanowires with high yield and tunable diameters by using Ni nanoparticles as the catalyst material on amorphous SiO2 substrates. The nanowires show superb electrical properties with field-effect electron mobility ~2700 cm2/Vs and ION/IOFF >103. The uniformity and purity of the grown InAs nanowires are further demonstrated by large-scale assembly of parallel arrays of nanowires on substrates via the contact printing process that enables high performance, “printable” transistors, capable of delivering 5 10 mA ON currents (~400 nanowires).     

Three-Dimensional Surface Texture Characterization of Portland Cement Concrete Pavements

Abstract:   This article investigates the effectiveness of different mathematical methods in describing the three-dimensional surface texture of Portland cement concrete (PCC) pavements. Ten PCC field cores of varying surface textures were included in the analysis. X-ray Computed Tomography (CT) was used to scan the upper portion of these cores, resulting in a stack of two-dimensional grayscale images. Image processing techniques were utilized to isolate the void pixels from the solid pixels and reconstruct the three-dimensional surface topography. The resulting three-dimensional surfaces were reduced to two-dimensional "map of heights" images, whereby the grayscale intensity of each pixel within the image represented the vertical location of the surface at that point with respect to the lowest point on the surface. The "map of heights" images were analyzed using four mathematical methods, namely the Hessian model, the Fast Fourier transform (FFT), the wavelet analysis, and the power spectral density (PSD). Results obtained using these methods were compared to the mean profile depth (MPD) computed in accordance with ASTM E1845 .